Enhancing Load Capacity Factor: The Influence of Financial Accessibility, AI Innovation, and Institutional Quality in the United States

Abstract

The investigation analyzes the impact of financial accessibility, AI innovation, urbanization, and institutional quality on the load capacity factor in the United States from 1990 to 2019. A series of stationarity tests were conducted to detect the presence of unit root problems, revealing a mixed order of integration with no significant unit root issues. To explore the cointegration among variables, the ARDL bounds test was employed, confirming long-run cointegration. The ARDL model's short-run and long-run estimations demonstrate that the Load Capacity Curve hypothesis holds in the United States, with a U-shaped relationship between income and load capacity factor. The results also reveal that financial accessibility, AI innovation, and institutional quality positively influence the load capacity factor in both the short and long run. Conversely, urbanization significantly reduces the load capacity factor over both time horizons. Furthermore, the study utilized further approaches, including Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR), all of which validated the ARDL estimation results. Diagnostic tests confirmed the robustness of the model, showing that the variables are free from specification errors, serial correlation, and heteroscedasticity. These findings provide valuable insights for policymakers aiming to enhance load capacity through financial and technological advancements while considering the implications of urbanization.